Recent efforts have shown interest in incorporating additional contextual information for meme analysis. [1]} proposed knowledge-enriched graph neural networks that use common-sense knowledge for offensive memes detection. [2]} focused on detecting COVID-19-related harmful memes and highlighted the challenge posed by the inherent biases within the existing multimodal systems. [3]} released another dataset focusing on US Politics and proposed a multimodal framework for harmful meme detection.
The Hateful Memes detection challenge by Facebook [4]} introduced the task of classifying a meme as hateful vs. non-hateful. Different approaches such as feature augmentation, attention mechanism, and multimodal loss re-weighting were attempted [5]}, [6]}, [7]}, [8]} as part of this task. [9]} studied hateful memes by highlighting the importance of visual cues such as structural template, graphic modality, causal depiction, etc.
